#手写体数字识别
#两层卷积层——一层全连接
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision      # 数据库模块
import matplotlib.pyplot as plt
import torch.nn.functional as F     # 激励函数都在这

torch.manual_seed(1)    # reproducible

# Hyper Parameters
EPOCH = 10           # 训练整批数据多少次, 为了节约时间, 我们只训练一次
BATCH_SIZE = 100
LR = 0.0001          # 学习率
DOWNLOAD_MNIST = False  # 如果你已经下载好了mnist数据就写上 False


# Mnist 手写数字
train_data = torchvision.datasets.MNIST(
    root='./mnist/',    # 保存或者提取位置
    train=True,  # this is training data
    transform=torchvision.transforms.ToTensor(),    # 转换 PIL.Image or numpy.ndarray 成
                                                    # torch.FloatTensor (C x H x W), 训练的时候 normalize 成 [0.0, 1.0] 区间
    download=DOWNLOAD_MNIST,          # 没下载就下载, 下载了就不用再下了
)
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)

# 批训练 50samples, 1 channel, 28x28 (50, 1, 28, 28)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

# 为了节约时间, 我们测试时只测试前2000个
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000]/255.   # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.test_labels[:2000]

class CNN_FC(nn.Module):
    def __init__(self):
        super(CNN_FC, self).__init__()
        self.conv1 = nn.Sequential(  # input shape (1, 28, 28)
            nn.Conv2d(
                in_channels=1,      # input height
                out_channels=6,    # n_filters
                kernel_size=5,      # filter size
                stride=1,           # filter movement/step
                padding=0,      # 如果想要 con2d 出来的图片长宽没有变化, padding=(kernel_size-1)/2 当 stride=1
            ),      # output shape (6, 24, 24)
            nn.BatchNorm2d(6),
            nn.ReLU(),    # activation
            nn.MaxPool2d(kernel_size=2),    # 在 2x2 空间里向下采样, output shape (6, 12, 12)
        )
        self.conv2 = nn.Sequential(  # input shape (6, 12, 12)
            nn.Conv2d(6, 16, 5, 1, 0),  # output shape (16, 8, 8)
            nn.BatchNorm2d(16),
            nn.ReLU(),  # activation
            nn.MaxPool2d(2),  # output shape (16, 4, 4)
        )
        self.conv3 = nn.Sequential(  # input shape (16, 4, 4)
            nn.Conv2d(16, 120, 3, 1, 0),  # output shape (120, 2, 2)
            nn.BatchNorm2d(120),
            nn.ReLU(),  # activation
            nn.MaxPool2d(2),  # output shape (120, 1, 1)
        )
        self.conv4 = nn.Sequential(  # input shape (120, 1, 1)
            nn.Conv2d(120, 120, 1, 1, 0),  # output shape (120, 1, 1)
            nn.BatchNorm2d(120),
            nn.ReLU(),  # activation
        )
        self.conv5 = nn.Sequential(  # input shape (120, 1, 1)
            nn.Conv2d(120, 120, 1, 1, 0),  # output shape (120, 1, 1)
            nn.BatchNorm2d(120),
            nn.ReLU(),  # activation
        )
        self.out = nn.Sequential(  # input shape (120, 1, 1)
            nn.Conv2d(120, 10, 1, 1, 0),  # output shape (10, 1, 1)
            #nn.ReLU(),  # activation
        )

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        output = self.out(x)
        output=torch.squeeze(output)
        return output

cnn = CNN_FC()
#print(cnn)  # net architecture


optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)   # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss()   # the target label is not one-hotted

# training and testing
for epoch in range(EPOCH):
    for step, (b_x, b_y) in enumerate(train_loader):   # 分配 batch data, normalize x when iterate train_loader
        output = cnn(b_x)               # cnn output
        loss = loss_func(output, b_y)   # cross entropy loss
        optimizer.zero_grad()           # clear gradients for this training step
        loss.backward()                 # backpropagation, compute gradients
        optimizer.step()                # apply gradients

        if step%50 == 0:
            test_output = cnn(test_x)
            pred_y=torch.max(F.softmax(test_output),1)[1].data.squeeze()
            accuarcy=sum(test_y==pred_y)/test_y.size(0)
            print('Epoch:',epoch,'| train loss: %.4f' % loss.data,'| test accuracy:%.4f' % accuarcy)


torch.save(cnn.state_dict(), 'cnn_fc.pkl')  # 只保存网络中的参数 (速度快, 占内存少)


test_output = cnn(test_x[:10])
pred_y = torch.max(F.softmax(test_output), 1)[1].data.numpy().squeeze()
print(pred_y, 'prediction number')
print(test_y[:10].numpy(), 'real number')
